An overview

Compyle allows users to execute a restricted subset of Python (almost similar to C) on a variety of HPC platforms. Currently we support multi-core execution using Cython, and OpenCL and CUDA for GPU devices.

An introduction to compyle in the context of writing a molecular dynamics simulator is available in our SciPy 2020 paper. You may also try Compyle online on a Google Colab notebook if you wish.

Users start with code implemented in a very restricted Python syntax, this code is then automatically transpiled, compiled and executed to run on either one CPU core, or multiple CPU cores or on a GPU. Compyle offers source-to-source transpilation, making it a very convenient tool for writing HPC libraries.

Compyle is not a magic bullet,

  • Do not expect that you may get a tremendous speedup.
  • Performance optimization can be hard and is platform specific. What works on the CPU may not work on the GPU and vice-versa. Compyle does not do anything to make this aspect easier. All the issues with memory bandwidth, cache, false sharing etc. still remain. Differences between memory architectures of CPUs and GPUs are not avoided at all – you still have to deal with it. But you can do so from the comfort of one simple programming language, Python.
  • Compyle makes it easy to write everything in pure Python and generate the platform specific code from Python. It provides a low-level tool to make it easy for you to generate whatever appropriate code.
  • The restrictions Compyle imposes make it easy for you to think about your algorithms in that context and thereby allow you to build functionality that exploits the hardware as you see fit.
  • Compyle hides the details of the backend to the extent possible. You can write your code in Python, you can reuse your functions and decompose your problem to maximize reuse. Traditionally you would end up implementing some code in C, some in Python, some in OpenCL/CUDA, some in string fragments that you put together. Then you’d have to manage each of the runtimes yourself, worry about compilation etc. Compyle minimizes that pain.
  • By being written in Python, we make it easy to assemble these building blocks together to do fairly sophisticated things relatively easily from the same language.
  • Compyle is fairly simple and does source translation making it generally easier to understand and debug. The core code-base is less than 7k lines of code.
  • Compyle has relatively simple dependencies, for CPU support it requires Cython and a C-compiler which supports OpenMP. On the GPU you need either PyOpenCL or PyCUDA. In addition it depends on NumPy and Mako.

While Compyle is simple and modest, it is quite powerful and convenient. In fact, Compyle has its origins in PySPH which is a powerful Python package supporting SPH, molecular dynamics, and other particle-based algorithms. The basic elements of Compyle are used in PySPH to automatically generate HPC code from code written in pure Python and execute it on multiple cores, and on GPUs without the user having to change any of their code. Compyle generalizes this code generation to make it available as a general tool.

These are the restrictions on the Python language that Compyle poses:

  • Functions with a C-syntax.
  • Function arguments must be declared using either type annotation or with a decorator or with default arguments.
  • No Python data structures, i.e. no lists, tuples, or dictionaries.
  • Contiguous Numpy arrays are supported but must be one dimensional.
  • No memory allocation is allowed inside these functions.
  • On OpenCL no recursion is supported.
  • All function calls must not use dotted names, i.e. don’t use math.sin, instead just use sin. This is because we do not perform any kind of name mangling of the generated code to make it easier to read.

Basically think of it as good old FORTRAN.

Technically we do support structs internally (we use it heavily in PySPH) but this is not yet exposed at the high-level and is very likely to be supported in the future.

Simple example

Enough talk, lets look at some code. Here is a very simple example:

from compyle.api import Elementwise, annotate, wrap, get_config
import numpy as np

@annotate(i='int', x='doublep', y='doublep', double='a,b')
def axpb(i, x, y, a, b):
    y[i] = a*sin(x[i]) + b

x = np.linspace(0, 1, 10000)
y = np.zeros_like(x)
a = 2.0
b = 3.0

backend = 'cython'
get_config().use_openmp = True
x, y = wrap(x, y, backend=backend)
e = Elementwise(axpb, backend=backend)
e(x, y, a, b)

This will execute the elementwise operation in parallel using OpenMP with Cython. The code is auto-generated, compiled and called for you transparently. The first time this runs, it will take a bit of time to compile everything but the next time, this is cached and will run much faster.

If you just change the backend = 'opencl', the same exact code will be executed using PyOpenCL and if you change the backend to 'cuda', it will execute via CUDA without any other changes to your code. This is obviously a very trivial example, there are more complex examples available as well.

To see the source code that is automatically generated for the above elementwise operation example use:


This will contain the sources that are generated based on the user code alone. To see all the sources created, use:


A word of warning though that this can be fairly long especially on a GPU and for other kind of operations may actually include multiple GPU kernels. This is largely for reference and debugging.

More examples

More complex examples (but still fairly simple) are available in the examples directory.

  • the above example but for openmp and opencl compared with serial showing that in some cases serial is actually faster than parallel!
  • shows a simple N-body code with two-dimensional point vortices. The code uses a simple elementwise operation and works with OpenMP and OpenCL.
  • shows the same code written in numba for comparison. In our benchmarks, Compyle is actually faster even in serial and in parallel it can be much faster when you use all cores.
  • shows how one can write a low-level OpenCL kernel in pure Python and use that. This also shows how you can allocate and use local (or shared) memory which is often very important for performance on GPGPUs. This code will only run via PyOpenCL.
  • Benchmarks the various vortex method results above for a comparison with numba.

Read on for more details about Compyle.

Citing Compyle

If you find Compyle useful or just want to read a paper on it, please see:

  • Aditya Bhosale and Prabhu Ramachandran, “Compyle: Python once, parallel computing anywhere”, Proceedings of the 19th Python in Science Conference (SciPy 2020), July, 2020, Austin, Texas, USA. doi:10.25080/Majora-342d178e-005 Won best poster SciPy 2020 Paper.

Accompanying the paper is the